Maximum Likelihood Estimation in Presence of Colored Noise

1. What are the limitations of maximum likelihood estimation?
2. When should I use maximum likelihood estimation?
3. How do you calculate maximum likelihood estimation?

What are the limitations of maximum likelihood estimation?

Disadvantages of Maximum Likelihood Estimation

Like other optimization problems, maximum likelihood estimation can be sensitive to the choice of starting values. Depending on the complexity of the likelihood function, the numerical estimation can be computationally expensive. Estimates can be biased in small samples.

When should I use maximum likelihood estimation?

The Maximum Likelihood Estimation framework can be used as a basis for estimating the parameters of many different machine learning models for regression and classification predictive modeling. This includes the logistic regression model.

How do you calculate maximum likelihood estimation?

Definition: Given data the maximum likelihood estimate (MLE) for the parameter p is the value of p that maximizes the likelihood P(data |p). That is, the MLE is the value of p for which the data is most likely. 100 P(55 heads|p) = ( 55 ) p55(1 − p)45. We'll use the notation p for the MLE.